1. National Himawari-8 Training Campaign – Phase 2 Tutorial Session 2 17 June 2015 Bodo Zeschke Australian VLab Centre of Excellence Point of Contact Should you use these resources please acknowledge the Australian VLab Centre of Excellence. In addition, you need to retain acknowledgement in the PowerPoint slides of EUMETSAT, the Japan Meteorological Agency, the Bureau of Meteorology and any other sources of information.

2. Contents of Tutorial Session 1, 17 th June 2015 A brief outline of the objectives of Phase 2 of the National Himawari-8 Training Campaign Exploring the Training and Assessment resources with emphasis on: o A brief introduction to Derived Products o Introducing the Blog page and Blog resources, with an example.

3. Contents of Tutorial Session 2, 17 th June 2015 A brief outline of the objectives of Phase 2 of the National Himawari-8 Training Campaign Exploring the Training and Assessment resources with emphasis on: o A brief introduction to Derived Products o Introducing the Blog page and Blog resources, with an example.

4. Assessment resources on the BMTC Moodle web page (20 questions out of a pool of 50+ Questions). National Himawari-8 Training Campaign Phase 2 Objectives Weekly tutorial sessions Easily accessible and user-friendly resources Himawari-8 Blog page. Links to other Blog resources New resources, including Derived Products etc.

5. Assessment resources on the BMTC Moodle web page (20 questions out of a pool of 50+ Questions). National Himawari-8 Training Campaign Phase 2 Objectives Weekly tutorial sessions Easily accessible and user-friendly resources Himawari-8 Blog page. Links to other Blog resources New resources, including Derived Products etc.

6. This Phase 2 of the Campaign will involve: Easily accessible and user-friendly resources for Stakeholder familiarisation with the new data from Himawari-8. New resources, including Himawari-8 case studies and an introduction to Derived Products. A Blog page for ongoing discussion of case studies pertaining to Himawari-8 data. Links to Blog resources from other organisations. Note that Blog resources were found popular amongst Forecasters and easy to use by Developers during the GOES-R Proving Ground Weekly tutorial sessions to consolidate the learning. Assessment resources on the BMTC Moodle web page. National Himawari-8 Training Campaign Phase 2 Objectives

7. Timelines for Himawari-8 Data and Training - 2015 JuneJulyAugustSeptemberOctoberNovember Himawari-8 data becomes available to the Bureau from July 7th (gradual (staged process) implementation of this) MTSAT-2 data will continue to be available Derived Products will start to become available from September (staged delivery likely) National Himawari-8 Training Campaign Phase 2 Phase 3 Science Week 2015

8. Timeline of Himawari-8 data in the Bureau (consultation with Denis Margetic, Leon Majewski and HIM89 Project Definition Document) MTSAT-2 imagery will continue to be provided to the Bureau in the interim until during October. Himawari-8 data operationally available from the 7 th July for the Bureau. Note that for the implementation of the new data, the satellite imagery will be delivered in a staged process first. The first Himawari-8 product to be released is likely to be the 10 minute 10.8 micron infrared channel (2km resolution). The bulk of the new satellite data is expected to be available to Forecasters when MTSAT data is switched off. The products (Derived Products) will then be delivered, from September onwards. There is likely to be a staged delivery of these as well. Advanced (Derived) products by end 2015 TC, Severe Storm detection etc. by end 2017 (note – this is very useful for our Training Campaign as Phase 2 can be extended)

9. Contents of Tutorial Session 1, 17 th June 2015 A brief outline of the objectives of Phase 2 of the National Himawari-8 Training Campaign Exploring the Training and Assessment resources with emphasis on: o A brief introduction to Derived Products o Introducing the Blog page and Blog resources, with an example.

10. National Himawari-8 Training Campaign Phase 2 - review http://www.virtuallab.bom.gov.au/training/hw-8-training/

11. National Himawari-8 Training Campaign Phase 2 - HITS Introduction, Resources and Case Studies Web links (released 2 nd June) Hits (by 16 th June) Introduction, Resources, Case Studies220 How Forecasters use the Himawari-8 data effectively 57-86 Tutorial Sessions and Feedback222

12. Reminder – the "How Forecasters can use the Himawari-8 data effectively" resource 1 2

13. 1 2

14. 1 2 Please have a look at these resources and forward me some feedback. This is a "living resource" which should expand with stakeholder input

15. Reminder – the "Red-Green-Blue (RGB) Product reference information" resource

16. RGB product reference.pdf files for easy Forecaster reference. Most include EUMETSAT ePort exercise. For Bureau staff these resources are very useful for the Moodle Quiz

17. National Himawari-8 Training Campaign Phase 2 Introduction, Resources and Case Studies

18. Contents of Tutorial Session 1, 17 th June 2015 A brief outline of the objectives of Phase 2 of the National Himawari-8 Training Campaign Exploring the Training and Assessment resources with emphasis on: o A brief introduction to Derived Products o Introducing the Blog page and Blog resources, with an example.

19. RGB Image Derived Product (TPW) NWP (TPW) Real “Semi” Real Not Real Derived Products (the Bureau plans to implement Himawari-8 Derived Products from September 2015) All NWP is wrong but some NWP is useful… It may look great but it’s not real… don’t be seduced! from “RGB Products versus Derived Products” Dr. Jochen Kerkmann, presented at WMO EUMETSAT RGB Satellite Products Workshop 2012 24-hour Microphysics RGB

20. Introducing Derived Products (1) Derived Products are generated: Using selected single band and band combinations of satellite data Applying some manipulation to this satellite data (statistical analysis, applying thresholds etc.) Utilising input from Numerical Weather Prediction models (NWP). Derived Products: Usually depend on some basic assumptions Provide quantitative information and are therefore less subjective than raw satellite data. Take time to compute From RGB Products and Derived Quantitative Products, Marianne Konig and Jochen Kerkmann EUMETSAT

21. Introducing Derived Products (2) There are different types of Derived Products: Products focussing on cloud-free regions (e.g. SST) Products focussing specifically on clouds (e.g. microphysics) Products using a multitude of information (time sequence, cloud evolution, e.g. Convective Initiation warnings, early warning of storm potential) From RGB Products and Derived Quantitative Products, Marianne Konig and Jochen Kerkmann EUMETSAT

22. Derived Products we have already looked at (EUMETRAIN ePort) Cloud Type Cloud Top Height Precipitating Clouds Convective Rain Rate

23. Derived Products we have already looked at (EUMETRAIN ePort) Cloud Type Cloud Top Height Precipitating Clouds Convective Rain Rate

24. Derived Product – Cloud Type Product (NWC SAF) (http://www.nwcsaf.org/HD/MainNS.jsp) From RGB Products versus Derived Products J. Kerkmann EumetSAT Fractional and high semitransparent clouds separated using T8.7µm- T10.8µm brightness temperature differences, but also variance in R0.6µm visible reflectance The remaining categories are distinguished through the comparison of their T10.8µm to NWP forecast temperatures at several pressure levels. T7.3µm and T8.7µm are also used to refine the separation between low and medium clouds.

25. The CT classification algorithm is based on the following approach: Main cloud types are separable within two sets: the fractional and high semitransparent clouds, from the low/medium/high clouds. These two systems are distinguished using spectral features : T10.8µm-T12.0µm, T3.9µm-T10.8µm (in night-time conditions only), R0.6µm (in day-time conditions only), and textural features (variance T10.8µm coupled to variance R0.6µm in daytime conditions). Within the first set, the fractional and high semitransparent are separated mainly using their T8.7µm-T10.8µm brightness temperature differences, but also their R0.6µm visible reflectance (in daytime conditions only). The remaining categories are distinguished through the comparison of their T10.8µm to NWP forecast temperatures at several pressure levels. Example of a Derived Product – Cloud Type Product (part 1) From RGB Products versus Derived Products J. Kerkmann EumetSAT also from http://www.nwcsaf.org/HD/MainNS.jsp

26. The CT classification algorithm is based on the following approach: T7.3µm and T8.7µm are also used to refine the separation between low and medium clouds, especially useful in case of low level thermal inversion. No separation between cumuliform and stratiform clouds is performed in the current version of CT. A separate processing is applied to compute a cloud phase flag, based on the use of CT cloud type, T8.7µm, T10.8µm (all illumination), R0.6µm and R1.6µm (at daytime only). Example of a Derived Product – Cloud Type Product (part 2) From RGB Products versus Derived Products J. Kerkmann EumetSAT also from http://www.nwcsaf.org/HD/MainNS.jsp

27. Bureau use of Derived Products (Phase 2) (from the Himawari 8 and 9 Project HIM89 Project Definition) PHASE 2A F OG M ASK SUITE [NOAA] V OLCANIC A SH RETRIEVAL SUITE [NOAA] C LOUD P ROPERTIES [NOAA] F OG P ROBABILITY SUITE ( BY LAYER ) [NOAA] V OLCANIC A SH P ROBABILITY SUITE [NOAA] C LOUD M ICROPHYSICAL P ROPERTIES SUITE [NOAA] A TMOSPHERIC M OTION V ECTORS SUITE S OLAR R ADIATION SUITE S EA S URFACE T EMPERATURE SUITE ( PART 1) PHASE 2B A IRCRAFT I CING P OTENTIAL V OLCANIC A SH OBJECT INFORMATION SUITE [NOAA] S EA S URFACE T EMPERATURE SUITE ( PART 2)

28. Bureau use of Derived Products (from the Himawari 8 and 9 Project HIM89 Project Definition) A TMOSPHERIC M OTION V ECTORS SUITE The AMV suite is being develop by CAWCR and Science and Engineering's Passive Remote Sensing team, and is expected to be finalised before end June 2015. AMV Vectors (u/v) NWP Selection Criteria (EE, QI) Data Processing Flags F OG M ASK SUITE [NOAA] Fog Mask Fog Depth F OG P ROBABILITY SUITE ( BY LAYER ) [NOAA] MVFR Fog Probability LIFR Fog Probability IFR Fog Probability IFR RH only Fog Probability V OLCANIC A SH RETRIEVAL SUITE [NOAA] Ash Top Temperature Ash Top Pressure Ash Top Height Ash Emissivity Ash Beta Ash Optical Depth IR Ash Mass Loading Ash Effective Radius S OLAR R ADIATION SUITE Solar Global Horizontal Irradiance Solar Direct Normal Irradiance Solar hourly Global Horizontal Exposure Solar hourly Direct Normal Exposure Solar Daily Global Horizontal Exposure V OLCANIC A SH P ROBABILITY SUITE [NOAA] Ash Probability IR VIS Ash Probability IR Cloud Emissivity Ch14 Cloud Beta 1112 Tot C LOUD P ROPERTIES [NOAA] Cloud Mask Cloud Top Temperature Cloud Top Pressure Cloud Top Height Cloud Type Cloud Phase C LOUD M ICROPHYSICAL P ROPERTIES SUITE [NOAA] Cloud Optical Depth (Visible) Cloud Particle Effective Radius Cloud Liquid Water Path Cloud Ice Water Path Cloud Albedo S EA S URFACE T EMPERATURE SUITE Sea Surface Temperature (Regression Channels 11 and 12) Sea Surface Temperature (Regression Channels 3, 11 and 12) Uncertainty A IRCRAFT I CING P OTENTIAL (PHASE 2) Aircraft Icing Potential S EA S URFACE T EMPERATURE SUITE (PHASE 2) Skin Surface Temperature (Physical Retrieval) [NOAA] V OLCANIC A SH OBJECT INFORMATION SUITE [NOAA] (PHASE 2) Ash Probability Volcanic Convection Detection

29. RGB Products and Derived Products From RGB Products versus Derived Products J. Kerkmann EumetSAT

30. Met-8, 1 Feb 2007, 01:30 UTC; NOAA-18, 1 Feb 2007, 1:22 UTC 24-h Microphys. RGB Cloud Type (MSG, SMHI) Cloud Type (NOAA, SMHI) Low Clouds / Fog (Night) – different satellite sensors Sweden Slide from “RGB Products versus Derived Products” Dr. Jochen Kerkmann, presented at WMO EUMETSAT RGB Satellite Products Workshop 2012

31. Cloud Type Products 24-h Micro RGB Low Clouds / Fog (Night) – different algorithms 1 November 2006, 4:00 UTC Arpege Model ECMWF Model Sweden Slide from “RGB Products versus Derived Products” Dr. Jochen Kerkmann, presented at WMO EUMETSAT RGB Satellite Products Workshop 2012

32. Cloud Type Derived Product – different satellite sensors, different algorithms In the previous two slides you can see some of the differences when using the Cloud Type Derived Product recipe for different satellite sensors and also for different Numerical Weather Prediction (NWP) models. Note that in the case of the different satellite sensors the Derived Product using the NOAA satellite data shows a large area over the Baltic Sea to the east of Sweden as cloud free. The Derived Product using the Meteosat Second Generation (MSG) satellite data shows very low cloud over much of the Baltic Sea instead. In the case of the different NWP models, the Derived Product using the Arpege model shows much of the land surface of Norway. The Derived Product using the ECMWF model indicates that this region is covered by very low cloud and also by some broken cloud. This is an important result because Derived Products can be obtained from both geostationary and polar orbiting satellite data so Forecaster understanding of the strengths and limitations of Derived Products from different sources is important for his/her correct interpretation of this data.

33. Exercise – compare the Night Microphysics RGB Product and the Cloud Type Product (EUMETRAIN ePort) Question: Give one advantage of the Derived product. Give one disadvantage of the Derived Product.

34. Advantages of Derived Products over RGB products Disadvantages of Derived Products over RGB products 1 – this data can be calibrated to assist people with colour blindness 2 – less subjective 3 – better for climatology studies 4 – not affected by viewing angle – good at all latitudes. 5 – products can be produced that focus upon particular properties. 1 – loss of texture of the cloud 2 – takes time to compute this – generated later 3 – dependent on NWP and other ancillary information 4 – difficult to animate (often noisy) 5 – not so good for detecting cloud boundaries and thin cloud (thin fog) 6 – reduced horizontal and vertical resolution (thresholding) Question – Advantages and dissadvantages of Derived Products over RGB products

35. Derived Quantitative Products for Climatology – overshooting stormtop climatology – Eastern Africa (Bedka, NASA) From "RGB Products and Derived Quantitative Products" Marianne König, Jochen Kerkmann,, presented at WMO EUMETSAT RGB Satellite Products Workshop 2012 0000-0045 UTC0600-0645 UTC 1200-1245 UTC 1800-1845 UTC

36. Contents of Tutorial Session 1, 17 th June 2015 A brief outline of the objectives of Phase 2 of the National Himawari-8 Training Campaign Exploring the Training and Assessment resources with emphasis on: o A brief introduction to Derived Products o Introducing the Blog page and Blog resources, with an example.

37. National Himawari-8 Training Campaign Phase 2 Blog Page http://www.virtuallab.bom.gov.au/training/hw-8-training/ Let's explore this together….

38. National Himawari-8 Training Campaign Phase 2 Blog Page (thanks to Denis Margetic, BOM for setting this up for me) As discussed, the process for the posts is as follows: Potential material sent to Bodo Zeschke, Australian Vlab Centre of Excellence Point of Contact http://www.virtuallab.bom.gov.au/contact-us/http://www.virtuallab.bom.gov.au/contact-us/ Bodo edits material as Blog editor Post created and published Investigate the first Blog (Thunderstorms and upper atmospheric Turbulence, MTSAT-1R rapid scan imagery, eastern Australia 22 nd January 2014): morning / afternoon thunderstorm development comparing rapid scan satellite data with RADAR data both in formative stage but also once the signal has been developed overshooting tops and intense RADAR echoes upper atmospheric turbulence vs monitoring transverse banding Investigate Blog links – Himawari-8 from CIMSS

39. Investigate Blog links – Himawari-8 from CIMSS http://cimss.ssec.wisc.edu/goes/blog/archives/category/himawari-8 Eruption of the Kuchinoerabu-jima volcano in Japan 29 May 2015 (animated 10 minute 0.5km visible channel data – low vs high altitude plume, various Derived Products – Ash Probability, Ash/Dust Cloud Height, Ash/Dust Loading, Ash/Dust Effective Radius) Singapore Airlines Flight SQ836:loss of engine power due to "ice crystal icing"? (at 39000 ft, rapid scan enhanced infrared imagery, rapid scan wv imagery – forecasters can recognise locations where ice crystal icing could be a problem, link to Ice crystal Icing) Other blogs – Typhoon Dolphin, (older posts) Super Typhoon Noul, Cyclone Quang, night-time glow of Hawaii's Kilauwea volcano. Blog's can be discussed during our tutorial sessions. The Blog contents can be developed into training case studies.

40. Summary A brief outline of the objectives of Phase 2 of the National Himawari-8 Training Campaign Exploring the Training and Assessment resources with emphasis on: o A brief introduction to Derived Products o Introducing the Blog page and Blog resources, with an example. Next Tutorial Session will be held Wednesday 24 th June 2015